Glymphatic dysfunction in patients with early-stage amyotrophic lateral sclerosis.

Recently, an astrocytic aquaporin 4-dependent drainage system, that is, the glymphatic system, has been identified in the live murine and human brain. Growing evidence suggests that glymphatic function is impaired in patients with several neurodegenerative diseases, including Alzheimer's and Parkinson's disease. As the third most common neurodegenerative disease, although animal studies have indicated that early glymphatic dysfunction is likely an important pathological mechanism underpinning amyotrophic lateral sclerosis (ALS), no available study has been conducted to thoroughly assess glymphatic function in vivo in ALS patients to date, particularly in patients with early-stage ALS. Thus, using diffusion tensor imaging analysis along the perivascular space (ALPS) index, an approximate measure of glymphatic function in vivo, we aimed to explore whether glymphatic function is impaired in patients with patients with early-stage ALS, and the diagnostic performance of the ALPS index in distinguishing between patients with early-stage ALS and healthy subjects. We also aimed to identify the relationships between glymphatic dysfunction and clinical disabilities and sleep problems in patients with early-stage ALS. In this retrospective study, King's Stage 1 ALS patients were defined as patients with early-stage ALS. We enrolled 56 patients with early-stage ALS and 32 age- and sex-matched healthy control subjects. All participants completed clinical screening, sleep assessment and ALPS index analysis. For the sleep assessment, the Pittsburgh Sleep Quality Index, Epworth Sleepiness Scale and polysomnography were used. Compared with healthy control subjects, patients with early-stage ALS had a significantly lower ALPS index after family-wise error correction (P < 0.05). Moreover, receiver operating characteristic analysis showed that the area under the curve for the ALPS index was 0.792 (95% confidence interval 0.700-0.884). Partial correlation analyses showed that the ALPS index was significantly correlated with clinical disability and sleep disturbances in patients with early-stage ALS. Multivariate analysis showed that sleep efficiency (r = 0.419, P = 0.002) and periodic limb movements in sleep index (r = -0.294, P = 0.017) were significant predictive factors of the ALPS index in patients with early-stage ALS. In conclusion, our study continues to support an important role for glymphatic dysfunction in ALS pathology, and we provide additional insights into the early diagnostic value of glymphatic dysfunction and its correlation with sleep disturbances in vivo in patients with early-stage ALS. Moreover, we suggest that early improvement of glymphatic function may be a promising strategy for slowing the neurodegenerative process in ALS. Future studies are needed to explore the diagnostic and therapeutic value of glymphatic dysfunction in individuals with presymptomatic-stage neurodegenerative diseases.

Common and distinct patterns underlying different linguistic tasks: multivariate disconnectome symptom mapping in poststroke patients.

Numerous studies have been devoted to neural mechanisms of a variety of linguistic tasks (e.g. speech comprehension and production). To date, however, whether and how the neural patterns underlying different linguistic tasks are similar or differ remains elusive. In this study, we compared the neural patterns underlying 3 linguistic tasks mainly concerning speech comprehension and production. To address this, multivariate regression approaches with lesion/disconnection symptom mapping were applied to data from 216 stroke patients with damage to the left hemisphere. The results showed that lesion/disconnection patterns could predict both poststroke scores of speech comprehension and production tasks; these patterns exhibited shared regions on the temporal pole of the left hemisphere as well as unique regions contributing to the prediction for each domain. Lower scores in speech comprehension tasks were associated with lesions/abnormalities in the superior temporal gyrus and middle temporal gyrus, while lower scores in speech production tasks were associated with lesions/abnormalities in the left inferior parietal lobe and frontal lobe. These results suggested an important role of the ventral and dorsal stream pathways in speech comprehension and production (i.e. supporting the dual stream model) and highlighted the applicability of the novel multivariate disconnectome-based symptom mapping in cognitive neuroscience research.

Individual variability in functional connectivity of human auditory cortex.

Individual variability in functional connectivity underlies individual differences in cognition and behaviors, yet its association with functional specialization in the auditory cortex remains elusive. Using resting-state functional magnetic resonance imaging data from the Human Connectome Project, this study was designed to investigate the spatial distribution of auditory cortex individual variability in its whole-brain functional network architecture. An inherent hierarchical axis of the variability was discerned, which radiates from the medial to lateral orientation, with the left auditory cortex demonstrating more pronounced variations than the right. This variability exhibited a significant correlation with the variations in structural and functional metrics in the auditory cortex. Four auditory cortex subregions, which were identified from a clustering analysis based on this variability, exhibited unique connectional fingerprints and cognitive maps, with certain subregions showing specificity to speech perception functional activation. Moreover, the lateralization of the connectional fingerprint exhibited a U-shaped trajectory across the subregions. These findings emphasize the role of individual variability in functional connectivity in understanding cortical functional organization, as well as in revealing its association with functional specialization from the activation, connectome, and cognition perspectives.

Cortical mapping of callosal connections in healthy young adults.

The corpus callosum (CC) is the principal white matter bundle supporting communication between the two brain hemispheres. Despite its importance, a comprehensive mapping of callosal connections is still lacking. Here, we constructed the first bidirectional population-based callosal connectional atlas between the midsagittal section of the CC and the cerebral cortex of the human brain by means of diffusion-weighted imaging tractography. The estimated connectional topographic maps within this atlas have the most fine-grained spatial resolution, demonstrate histological validity, and were reproducible in two independent samples. This new resource, a complete and comprehensive atlas, will facilitate the investigation of interhemispheric communication and come with a user-friendly companion online tool (CCmapping) for easy access and visualization of the atlas.

Callosal Fiber Length Scales with Brain Size According to Functional Lateralization, Evolution, and Development.

Brain size significantly impacts the organization of white matter fibers. Fiber length scaling, the degree to which fiber length varies according to brain size, was overlooked. We investigated how fiber lengths within the corpus callosum, the most prominent white matter tract, vary according to brain size. The results showed substantial variation in length scaling among callosal fibers, replicated in two large healthy cohorts (∼2000 human subjects, including both sexes). The underscaled callosal fibers mainly connected the precentral gyrus and parietal cortices, whereas the overscaled callosal fibers mainly connected the prefrontal cortices. The variation in such length scaling was biologically meaningful: larger scaling corresponded to larger neurite density index but smaller fractional anisotropy values; cortical regions connected by the callosal fibers with larger scaling were more lateralized functionally as well as phylogenetically and ontogenetically more recent than their counterparts. These findings highlight an interaction between interhemispheric communication and organizational and adaptive principles underlying brain development and evolution.SIGNIFICANCE STATEMENT Brain size varies across evolution, development, and individuals. Relative to small brains, the neural fiber length in large brains is inevitably increased, but the degree of such increase may differ between fiber tracts. Such a difference, if it exists, is valuable for understanding adaptive neural principles in large versus small brains during evolution and development. The present study showed a substantial difference in the length increase between the callosal fibers that connect the two hemispheres, replicated in two large healthy cohorts. Together, our study demonstrates that reorganization of interhemispheric fibers length according to brain size is intrinsically related to fiber composition, functional lateralization, cortical myelin content, and evolutionary and developmental expansion.

Altered resting-state functional connectivity in the prefrontal cortex is related to the development of dyscalculia in patients with Turner syndrome.

AIM: Patients with Turner syndrome have a high rate of developmental dyscalculia, but the underlying neurocognitive mechanisms are not well-understood. Some studies have implicated visuospatial impairments in patients with Turner syndrome, but others have focused on poor procedural skills in patients with Turner syndrome. This study used brain imaging data to test these two alternative views. METHODS: This study recruited 44 girls with Turner syndrome (mean age, 12.91 years; SD, 2.02), with 13 (29.5%) of them meeting the criterion for developmental dyscalculia, and 14 normally developing girls (mean age, 14.26 years; SD, 2.18) as a comparison group. All participants were given basic mathematical ability tests and an intelligence test and were scanned using magnetic resonance imaging. We compared patients with Turner syndrome who had dyscalculia, patients with Turner syndrome who did not have dyscalculia, and the normal controls in terms of brain structures and resting-state functional activity. RESULTS: Compared with normal controls, both groups of patients with Turner syndrome (with or without dyscalculia) showed similarly altered functional connectivity in the occipitoparietal dorsal stream. Importantly, compared with patients with Turner syndrome without dyscalculia and normal controls, patients with Turner syndrome with dyscalculia showed decreased functional connectivity between the prefrontal and the lateral occipital cortex. CONCLUSION: We concluded that both groups of patients with Turner syndrome shared visual deficits, and patients with Turner syndrome with dyscalculia had a deficit in frontal cortex-based higher cognitive processing. It is not their visuospatial deficits but rather their deficits in higher cognitive processing that are responsible for the development of dyscalculia in patients with Turner syndrome.

Interhemispheric Relationship of Genetic Influence on Human Brain Connectivity.

To understand the origins of interhemispheric differences and commonalities/coupling in human brain wiring, it is crucial to determine how homologous interregional connectivities of the left and right hemispheres are genetically determined and related. To address this, in the present study, we analyzed human twin and pedigree samples with high-quality diffusion magnetic resonance imaging tractography and estimated the heritability and genetic correlation of homologous left and right white matter (WM) connections. The results showed that the heritability of WM connectivity was similar and coupled between the 2 hemispheres and that the degree of overlap in genetic factors underlying homologous WM connectivity (i.e., interhemispheric genetic correlation) varied substantially across the human brain: from complete overlap to complete nonoverlap. Particularly, the heritability was significantly stronger and the chance of interhemispheric complete overlap in genetic factors was higher in subcortical WM connections than in cortical WM connections. In addition, the heritability and interhemispheric genetic correlations were stronger for long-range connections than for short-range connections. These findings highlight the determinants of the genetics underlying WM connectivity and its interhemispheric relationships, and provide insight into genetic basis of WM connectivity asymmetries in both healthy and disease states.

Sex-related human brain asymmetry in hemispheric functional gradients.

The left and right hemispheres of the human brain are two connected but relatively independent functional modules; they show multidimensional asymmetries ranging from particular local brain unit properties to entire hemispheric connectome topology. To date, however, it remains largely unknown whether and how hemispheric functional hierarchical structures differ between hemispheres. In the present study, we adopted a newly developed resting-state (rs) functional connectivity (FC)-based gradient approach to evaluate hemispheric functional hierarchical structures and their asymmetries in right-handed healthy young adults. Our results showed an overall mirrored principal functional gradient between hemispheres, with the sensory cortex and the default-mode network (DMN) anchored at the two opposite ends of the gradient. Interestingly, the left hemisphere showed a significantly larger full range of the principal gradient in both males and females, with males exhibiting greater leftward asymmetry. Similarly, the principal gradient component scores of two regions around the middle temporal gyrus and posterior orbitofrontal cortex exhibited similar hemisphere × sex interaction effects: a greater degree of leftward asymmetry in males than in females. Moreover, we observed significant main hemisphere and sex effects in distributed regions across the entire hemisphere. All these results are reproducible and robust between test-retest rs-fMRI sessions. Our findings provide evidence of functional gradients that enhance the present understanding of human brain asymmetries in functional organization and highlight the impact of sex on hemispheric functional gradients and their asymmetries.

Characterizing the hyper- and hypometabolism in temporal lobe epilepsy using multivariate machine learning.

Mesial temporal lobe epilepsy (MTLE) is the most common type of focal epilepsy, presenting both structural and metabolic abnormalities in the ipsilateral mesial temporal lobe. While it has been demonstrated that the metabolic abnormalities in MTLE actually extend beyond the epileptogenic zone, how such multidimensional information is associated with the diagnosis of MTLE remains to be tested. Here, we explore the whole-brain metabolic patterns in 23 patients with MTLE and 24 healthy controls using [18 F]fluorodeoxyglucose PET imaging. Based on a multivariate machine learning approach, we demonstrate that the brain metabolic patterns can discriminate patients with MTLE from controls with a superior accuracy (>95%). Importantly, voxels showing the most extreme contributing weights to the classification (i.e., the most important regional predictors) distribute across both hemispheres, involving both ipsilateral negative weights over the anterior part of lateral and medial temporal lobe, posterior insula, and lateral orbital frontal gyrus, and contralateral positive weights over the anterior frontal lobe, temporal lobe, and lingual gyrus. Through region-of-interest analyses, we verify that in patients with MTLE, the negatively weighted regions are hypometabolic, and the positively weighted regions are hypermetabolic, compared to controls. Interestingly, despite that both hypo- and hypermetabolism have mutually contributed to our model, they may reflect different pathological and/or compensative responses. For instance, patients with earlier age at epilepsy onset present greater hypometabolism in the ipsilateral inferior temporal gyrus, while we find no evidence of such association with hypermetabolism. In summary, quantitative models utilizing multidimensional brain metabolic information may provide additional assistance to presurgical workups in TLE.

Associations between hemispheric asymmetry and schizophrenia-related risk genes in people with schizophrenia and people at a genetic high risk of schizophrenia.

BACKGROUND: Schizophrenia is considered a polygenic disorder. People with schizophrenia and those with genetic high risk of schizophrenia (GHR) have presented with similar neurodevelopmental deficits in hemispheric asymmetry. The potential associations between neurodevelopmental abnormalities and schizophrenia-related risk genes in both schizophrenia and those with GHR remains unclear. AIMS: To investigate the shared and specific alternations to the structural network in people with schizophrenia and those with GHR. And to identify an association between vulnerable structural network alternation and schizophrenia-related risk genes. METHOD: A total of 97 participants with schizophrenia, 79 participants with GHR and 192 healthy controls, underwent diffusion tensor imaging (DTI) scans at a single site. We used graph theory to characterise hemispheric and whole-brain structural network topological metrics. For 26 people in the schizophrenia group and 48 in the GHR group with DTI scans we also calculated their schizophrenia-related polygenic risk scores (SZ-PRSs). The correlations between alterations to the structural network and SZ-PRSs were calculated. Based on the identified genetic-neural association, bioinformatics enrichment was explored. RESULTS: There were significant hemispheric asymmetric deficits of nodal efficiency, global and local efficiency in the schizophrenia and GHR groups. Hemispheric asymmetric deficit of local efficiency was significantly positively correlated with SZ-PRSs in the schizophrenia and GHR groups. Bioinformatics enrichment analysis showed that these risk genes may be linked to signal transduction, neural development and neuron structure. The schizophrenia group showed a significant decrease in the whole-brain structural network. CONCLUSIONS: The shared asymmetric deficits in people with schizophrenia and those with GHR, and the association between anomalous asymmetry and SZ-PRSs suggested a vulnerability imaging marker regulated by schizophrenia-related risk genes. Our findings provide new insights into asymmetry regulated by risk genes and provides a better understanding of the genetic-neural pathological underpinnings of schizophrenia.

Semantic representation in the white matter pathway.

Object conceptual processing has been localized to distributed cortical regions that represent specific attributes. A challenging question is how object semantic space is formed. We tested a novel framework of representing semantic space in the pattern of white matter (WM) connections by extending the representational similarity analysis (RSA) to structural lesion pattern and behavioral data in 80 brain-damaged patients. For each WM connection, a neural representational dissimilarity matrix (RDM) was computed by first building machine-learning models with the voxel-wise WM lesion patterns as features to predict naming performance of a particular item and then computing the correlation between the predicted naming score and the actual naming score of another item in the testing patients. This correlation was used to build the neural RDM based on the assumption that if the connection pattern contains certain aspects of information shared by the naming processes of these two items, models trained with one item should also predict naming accuracy of the other. Correlating the neural RDM with various cognitive RDMs revealed that neural patterns in several WM connections that connect left occipital/middle temporal regions and anterior temporal regions associated with the object semantic space. Such associations were not attributable to modality-specific attributes (shape, manipulation, color, and motion), to peripheral picture-naming processes (picture visual similarity, phonological similarity), to broad semantic categories, or to the properties of the cortical regions that they connected, which tended to represent multiple modality-specific attributes. That is, the semantic space could be represented through WM connection patterns across cortical regions representing modality-specific attributes.

Individualized Cortical Parcellation Based on Diffusion MRI Tractography.

The spatial topological properties of cortical regions vary across individuals. Connectivity-based functional and anatomical cortical mapping in individuals will facilitate research on structure-function relationships. However, individual-specific cortical topographic properties derived from anatomical connectivity are less explored than those based on functional connectivity. We aimed to develop a novel individualized anatomical connectivity-based parcellation framework and investigate individual differences in spatial topographic features of cortical regions using diffusion magnetic resonance imaging (dMRI) tractography. Using a high-quality, repeated-session dMRI dataset (42 subjects, 2 sessions per subject), cortical parcels were derived through in vivo anatomical connectivity-based parcellation. These individual-specific parcels demonstrated good within-individual reproducibility and reflected interindividual differences in anatomical brain organization. Connectivity in these individual-specific parcels was significantly more homogeneous than that based on the group atlas. We found that the position, size, and topography of these anatomical parcels were highly variable across individuals and demonstrated nonredundant information about individual differences. Finally, we found that intersubject variability in anatomical connectivity was correlated with the diversity of anatomical connectivity patterns. Overall, we identified cortical parcels that show homogeneous anatomical connectivity patterns. These parcels displayed marked intersubject spatial variability, which may be used in future functional studies to reveal structure-function relationships in the human brain.

Resting-state connectome-based support-vector-machine predictive modeling of internet gaming disorder.

Internet gaming disorder (IGD), a worldwide mental health issue, has been widely studied using neuroimaging techniques during the last decade. Although dysfunctions in resting-state functional connectivity have been reported in IGD, mapping relationships from abnormal connectivity patterns to behavioral measures have not been fully investigated. Connectome-based predictive modeling (CPM)-a recently developed machine-learning approach-has been used to examine potential neural mechanisms in addictions and other psychiatric disorders. To identify the resting-state connections associated with IGD, we modified the CPM approach by replacing its core learning algorithm with a support vector machine. Resting-state functional magnetic resonance imaging (fMRI) data were acquired in 72 individuals with IGD and 41 healthy comparison participants. The modified CPM was conducted with respect to classification and regression. A comparison of whole-brain and network-based analyses showed that the default-mode network (DMN) is the most informative network in predicting IGD both in classification (individual identification accuracy = 78.76%) and regression (correspondence between predicted and actual psychometric scale score: r = 0.44, P < 0.001). To facilitate the characterization of the aberrant resting-state activity in the DMN, the identified networks have been mapped into a three-subsystem division of the DMN. Results suggest that individual differences in DMN function at rest could advance our understanding of IGD and variability in disorder etiology and intervention outcomes.

Neurobiological commonalities and distinctions among 3 major psychiatric disorders: a graph theoretical analysis of the structural connectome

Background: White matter network alterations have increasingly been implicated in major depressive disorder, bipolar disorder and schizophrenia. The aim of this study was to identify shared and distinct white matter network alterations among the 3 disorders. Methods: We used analysis of covariance, with age and gender as covariates, to investigate white matter network alterations in 123 patients with schizophrenia, 123 with bipolar disorder, 124 with major depressive disorder and 209 healthy controls. Results: We found significant group differences in global network efficiency (F = 3.386, p = 0.018), nodal efficiency (F = 8.015, p < 0.001 corrected for false discovery rate [FDR]) and nodal degree (F = 5.971, pFDR < 0.001) in the left middle occipital gyrus, as well as nodal efficiency (F = 6.930, pFDR < 0.001) and nodal degree (F = 5.884, pFDR < 0.001) in the left postcentral gyrus. We found no significant alterations in patients with major depressive disorder. Post hoc analyses revealed that compared with healthy controls, patients in the schizophrenia and bipolar disorder groups showed decreased global network efficiency, nodal efficiency and nodal degree in the left middle occipital gyrus. Furthermore, patients in the schizophrenia group showed decreased nodal efficiency and nodal degree in the left postcentral gyrus compared with healthy controls. Limitations: Our findings could have been confounded in part by treatment differences. Conclusion: Our findings implicate graded white matter network alterations across the 3 disorders, enhancing our understanding of shared and distinct pathophysiological mechanisms across diagnoses and providing vital insights into neuroimaging-based methods for diagnosis and research.

Hemispheric Module-Specific Influence of the X Chromosome on White Matter Connectivity: Evidence from Girls with Turner Syndrome.

Turner syndrome (TS) is caused by the congenital absence of all or part of one of the X chromosomes in females, offering a valuable human "knockout model" to study the functioning patterns of the X chromosome in the human brain. Little is known about whether and how the loss of the X chromosome influences the brain structural wiring patterns in human. We acquired a multimodal MRI dataset and cognitive assessments from 22 girls with TS and 21 age-matched control girls to address these questions. Hemispheric white matter (WM) networks and modules were derived using refined diffusion MRI tractography. Statistical comparisons revealed a reduced topological efficiency of both hemispheric networks and bilateral parietal modules in TS girls. Specifically, the efficiency of right parietal module significantly mediated the effect of the X chromosome on working memory performance, indicating that X chromosome loss impairs working memory performance by disrupting this module. Additionally, TS girls showed structural and functional connectivity decoupling across specific within- and between-modular connections, predominantly in the right hemisphere. These findings provide novel insights into the functional pathways in the brain that are regulated by the X chromosome and highlight a module-specific genetic contribution to WM connectivity in the human brain.

Network analysis reveals disrupted functional brain circuitry in drug-naive social anxiety disorder.

Social anxiety disorder (SAD) is a common and disabling condition characterized by excessive fear and avoidance of public scrutiny. Psychoradiology studies have suggested that the emotional and behavior deficits in SAD are associated with abnormalities in regional brain function and functional connectivity. However, little is known about whether intrinsic functional brain networks in patients with SAD are topologically disrupted. Here, we collected resting-state fMRI data from 33 drug-naive patients with SAD and 32 healthy controls (HC), constructed functional networks with 34 predefined regions based on previous meta-analytic research with task-based fMRI in SAD, and performed network-based statistic and graph-theory analyses. The network-based statistic analysis revealed a single connected abnormal circuitry including the frontolimbic circuit (termed the "fear circuit", including the dorsolateral prefrontal cortex, ventral medial prefrontal cortex and insula) and posterior cingulate/occipital areas supporting perceptual processing. In this single altered network, patients with SAD had higher functional connectivity than HC. At the global level, graph-theory analysis revealed that the patients exhibited a lower normalized characteristic path length than HC, which suggests a disorder-related shift of network topology toward randomized configurations. SAD-related deficits in nodal degree, efficiency and participation coefficient were detected in the parahippocampal gyrus, posterior cingulate cortex, dorsolateral prefrontal cortex, insula and the calcarine sulcus. Aspects of abnormal connectivity were associated with anxiety symptoms. These findings highlight the aberrant topological organization of functional brain network organization in SAD, which provides insights into the neural mechanisms underlying excessive fear and avoidance of social interactions in patients with debilitating social anxiety.

Effects of hypogonadism on brain development during adolescence in girls with Turner syndrome.

Gonadal steroids play an important role in brain development, particularly during puberty. Girls with Turner syndrome (TS), a genetic disorder characterized by the absence of all or part of the second X chromosome, mostly present a loss of ovarian function and estrogen deficiency, as well as neuroanatomical abnormalities. However, few studies have attempted to isolate the indirect effects of hormones from the direct genetic effects of X chromosome insufficiency. Brain structural (i.e., gray matter [GM] morphology and white matter [WM] connectivity) and functional phenotypes (i.e., resting-state functional measures) were investigated in 23 adolescent girls with TS using multimodal MRI to assess the role of hypogonadism in brain development in TS. Specifically, all girls with TS were divided into a hormonally subnormal group and an abnormal subgroup according to their serum follicle-stimulating hormone (FSH) levels, with the karyotypes approximately matched between the two groups. Statistical analyses revealed significant effects of the "group-by-age" interaction on GM volume around the left medial orbitofrontal cortex and WM diffusion parameters around the bilateral corticospinal tract, anterior thalamic radiation, left superior longitudinal fasciculus, and cingulum bundle, but no significant "group-by-age" or group differences were observed in resting-state functional measures. Based on these findings, estrogen deficiency has a nontrivial impact on the development of the brain structure during adolescence in girls with TS. Our present study provides novel insights into the mechanism by which hypogonadism influences brain development during adolescence in girls with TS, and highlights the important role of estrogen replacement therapy in treating TS.

Individualized Prediction of Reading Comprehension Ability Using Gray Matter Volume.

Reading comprehension is a crucial reading skill for learning and putatively contains 2 key components: reading decoding and linguistic comprehension. Current understanding of the neural mechanism underlying these reading comprehension components is lacking, and whether and how neuroanatomical features can be used to predict these 2 skills remain largely unexplored. In the present study, we analyzed a large sample from the Human Connectome Project (HCP) dataset and successfully built multivariate predictive models for these 2 skills using whole-brain gray matter volume features. The results showed that these models effectively captured individual differences in these 2 skills and were able to significantly predict these components of reading comprehension for unseen individuals. The strict cross-validation using the HCP cohort and another independent cohort of children demonstrated the model generalizability. The identified gray matter regions contributing to the skill prediction consisted of a wide range of regions covering the putative reading, cerebellum, and subcortical systems. Interestingly, there were gender differences in the predictive models, with the female-specific model overestimating the males' abilities. Moreover, the identified contributing gray matter regions for the female-specific and male-specific models exhibited considerable differences, supporting a gender-dependent neuroanatomical substrate for reading comprehension.

Domain Selectivity in the Parahippocampal Gyrus Is Predicted by the Same Structural Connectivity Patterns in Blind and Sighted Individuals.

Human ventral occipital temporal cortex contains clusters of neurons that show domain-preferring responses during visual perception. Recent studies have reported that some of these clusters show surprisingly similar domain selectivity in congenitally blind participants performing nonvisual tasks. An important open question is whether these functional similarities are driven by similar innate connections in blind and sighted groups. Here we addressed this question focusing on the parahippocampal gyrus (PHG), a region that is selective for large objects and scenes. Based on the assumption that patterns of long-range connectivity shape local computation, we examined whether domain selectivity in PHG is driven by similar structural connectivity patterns in the two populations. Multiple regression models were built to predict the selectivity of PHG voxels for large human-made objects from white matter (WM) connectivity patterns in both groups. These models were then tested using independent data from participants with similar visual experience (two sighted groups) and using data from participants with different visual experience (blind and sighted groups). Strikingly, the WM-based predictions between blind and sighted groups were as successful as predictions between two independent sighted groups. That is, the functional selectivity for large objects of a PHG voxel in a blind participant could be accurately predicted by its WM pattern using the connection-to-function model built from the sighted group data, and vice versa. Regions that significantly predicted PHG selectivity were located in temporal and frontal cortices in both sighted and blind populations. These results show that the large-scale network driving domain selectivity in PHG is independent of vision.SIGNIFICANCE STATEMENT Recent studies have reported intriguingly similar domain selectivity in sighted and congenitally blind individuals in regions within the ventral visual cortex. To examine whether these similarities originate from similar innate connectional roots, we investigated whether the domain selectivity in one population could be predicted by the structural connectivity pattern of the other. We found that the selectivity for large objects of a PHG voxel in a blind participant could be predicted by its structural connectivity pattern using the connection-to-function model built from the sighted group data, and vice versa. These results reveal that the structural connectivity underlying domain selectivity in the PHG is independent of visual experience, providing evidence for nonvisual representations in this region.

The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features.

Individualized behavioral/cognitive prediction using machine learning (ML) regression approaches is becoming increasingly applied. The specific ML regression algorithm and sample size are two key factors that non-trivially influence prediction accuracies. However, the effects of the ML regression algorithm and sample size on individualized behavioral/cognitive prediction performance have not been comprehensively assessed. To address this issue, the present study included six commonly used ML regression algorithms: ordinary least squares (OLS) regression, least absolute shrinkage and selection operator (LASSO) regression, ridge regression, elastic-net regression, linear support vector regression (LSVR), and relevance vector regression (RVR), to perform specific behavioral/cognitive predictions based on different sample sizes. Specifically, the publicly available resting-state functional MRI (rs-fMRI) dataset from the Human Connectome Project (HCP) was used, and whole-brain resting-state functional connectivity (rsFC) or rsFC strength (rsFCS) were extracted as prediction features. Twenty-five sample sizes (ranged from 20 to 700) were studied by sub-sampling from the entire HCP cohort. The analyses showed that rsFC-based LASSO regression performed remarkably worse than the other algorithms, and rsFCS-based OLS regression performed markedly worse than the other algorithms. Regardless of the algorithm and feature type, both the prediction accuracy and its stability exponentially increased with increasing sample size. The specific patterns of the observed algorithm and sample size effects were well replicated in the prediction using re-testing fMRI data, data processed by different imaging preprocessing schemes, and different behavioral/cognitive scores, thus indicating excellent robustness/generalization of the effects. The current findings provide critical insight into how the selected ML regression algorithm and sample size influence individualized predictions of behavior/cognition and offer important guidance for choosing the ML regression algorithm or sample size in relevant investigations.